Computer Science

Algorithmics and Computational Intelligence Group (ACI)

The research of the laboratory is centered around techniques and methods for algorithm design and computational intelligence, with the emphasis on both theory and applications. The foundations of the research are machine learning, probabilistic inference, discrete mathematics and theoretical computer science. In particular, the research of kernel methods, Bayesian analysis, probabilistic and information-theoretical modeling, combinatorial algorithms and intelligent systems has been pursued. The research of the laboratory is based on the long tradition of combining basic research on algorithm development and analysis with active cooperation with companies and academic partners on solving real-life problems by the use of combinatorial optimization and latest techniques on computational intelligence methods.

Selected research topics on algorithmics and computational intelligence:

  • Classification and regression methods
  • Clustering methods
  • Combinatorial algorithms and applications
  • Cross-validation methods
  • Data compression
  • Feature selection methods for high-dimensional data
  • Industrial algorithms
  • Information retrieval
  • Information theoretic methods
  • Multi-task and transfer learning
  • Preference learning and ranking
  • Probabilistic Bayesian methods
  • String algorithms
  • Tensor product kernels for pairwise learning

Recent Publications

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